# Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import plotly.express as px
import plotly.io as pio
import plotly
from ydata_profiling import  ProfileReport
import PyQt5 as qt
from IPython.display import display, Markdown
#Enable graphing inside jupytor
#pip install PyQt5 #Install it if not installed
get_ipython().run_line_magic('matplotlib', 'inline')
matplotlib.get_backend()

'module://matplotlib_inline.backend_inline'

# Load Data

df = pd.read_excel('maintenance_cleaned_extended.xlsx')

# Visualization Functions

## Bar, Scatter, Line charts

def myPlot(data,plotType,title):
    data = data.sort_values(ascending=True)
    xs = data.index.astype(str)  
    ys = data.values
    if plotType == 'bar':
        fig = px.bar(data_frame=data, x = xs, y = ys,color=ys,title=title+' Analysis')
    elif plotType == 'scatter':
        fig = px.scatter(data_frame=data, x = xs, y = ys,color=ys,title=title+' Analysis')
    elif plotType == 'line':
        fig = px.line(data_frame=data, x = xs, y = ys,title=title+' Analysis')
    fig.update_layout(title_x=0.45)
    fig.show()

def myPlot1(data, xs, ys, clr, plotType, title, sort_by=None, ascending=True):
    if sort_by is not None:
        data_sorted = data.sort_values(by=sort_by, ascending=ascending)
    else:
        data_sorted = data
    xt = str(xs)
    yt = str(ys)
    xs = data_sorted[xs]
    ys = data_sorted[ys]
    clr = data_sorted[clr].astype(str) if clr else None
    if plotType == 'bar':
        fig = px.bar(x=xs, y=ys, color=clr, title=title + ' Analysis')
    elif plotType == 'scatter':
        fig = px.scatter(x=xs, y=ys, color=clr, title=title + ' Analysis')
    elif plotType == 'line':
        fig = px.line(x=xs, y=ys, color=clr, title=title + ' Analysis')
    fig.update_layout(title_x=0.5)
    fig.update_layout(xaxis_title=xt, yaxis_title=yt)
    fig.show()

def myPlot2(data, plotType, title):
    xs = data.index.astype(str)  # Index (x-axis)
    ys = data.values             # Values (y-axis)
    # Plot based on the plotType
    if plotType == 'bar':
        fig = px.bar(x=xs, y=ys, color=ys, title=title + ' Analysis')
    elif plotType == 'scatter':
        fig = px.scatter(x=xs, y=ys, color=ys, title=title + ' Analysis')
    elif plotType == 'line':
        fig = px.line(x=xs, y=ys, title=title + ' Analysis')
    # Center the title
    fig.update_layout(title_x=0.5)
    fig.show()

def myBoxPlot(data,x,y,color,title):
    fig = px.box(data, x=x, y=y, color=color, title=title)
    fig.update_layout(
        title_x=0.5,
        xaxis_title=str(x),
        yaxis_title=str(y)
    )
    fig.show()

## Sunburst chart

def mySunBurst(data, name, value, title):
    fig = px.sunburst(
        data_frame=data,
        #path=['cost_category', 'damage type'],   # Add both cost_category and damage type to the hierarchy
        path=name,
        values=value,  # Define the values (damage_count)
        title=title+' Analysis'
    )
    fig.update_layout(title_x=0.45)
    fig.show()

## Pie chart

def myPie(data,title_prefix):
    name  = data.index
    value = data.values
    fig = px.pie(data_frame=data,
                 names = name, 
                 values = value,
                 title ='Top 5 '+ title_prefix +' Analysis'
                )
    fig.update_layout(title_x=0.5)

    fig.show()

## Combine DataFrames

def combine(data,first_field,first_field_count,field_grouped_on,resulting_field_value):
    data_first_cat = data[first_field].value_counts().reset_index()
    data_first_cat.columns = [first_field,first_field_count]
    data_merged = data.groupby([first_field])[field_grouped_on].sum().reset_index(name=resulting_field_value)
    data_merged = data_merged.merge(data_first_cat,on=first_field)
    return first_field_count, resulting_field_value, data_merged

## Bi - Variance Analysis

### Cost Category Insights

#### Distribution of damage types across different cost categories

damageType_costCategory = df.groupby(['damage type','cost_category']).size().reset_index(name='Count').sort_values(by='Count')
myPlot1(damageType_costCategory,'damage type','Count','cost_category','bar','Distribution of damage types across different cost categories', sort_by=None, ascending=True)

#### Relationship between cost categories and car models

car_costCategory = df.groupby(['car','cost_category']).size().reset_index(name='Count').sort_values(by=['car'])
myPlot1(car_costCategory,'car','Count','cost_category','bar','Relationship between cost categories and car models', sort_by=None, ascending=True)

#### Comparison of service duration across different cost categories

myBoxPlot(data=df,x='cost_category',y='service_duration',color='cost_category',title='Comparison of service duration across different cost categories')

#### Cost category breakdown by location

location_costCategory = df.groupby(['location','cost_category']).size().reset_index(name='Count').sort_values(by=['location'])
myPlot1(location_costCategory,'location','Count','cost_category','bar','Cost category breakdown by location', sort_by=None, ascending=True)

#### Trends in cost categories over time

df['year_month'] = df['date ready'].dt.year.astype(str) + '_' + df['date ready'].dt.month.astype(str).str.zfill(2)
date_costCategory = df.groupby(['year_month','cost_category']).size().reset_index(name='Frequency').sort_values(by='year_month')
myPlot1(date_costCategory,'year_month','Frequency','cost_category','line','Trends in cost categories over time', sort_by=None, ascending=True)